典型文献
Bayesian machine learning-based method for prediction of slope failure time
文献摘要：
The data-driven phenomenological models based on deformation measurements have been widely utilized to predict the slope failure time(SFT).The observational and model uncertainties could lead the predicted SFT calculated from the phenomenological models to deviate from the actual SFT Currently,very limited study has been conducted on how to evaluate the effect of such uncertainties on SFT pre-diction.In this paper,a comprehensive slope failure database was compiled.A Bayesian machine learning(BML)-based method was developed to learn the model and observational uncertainties involved in SFT prediction,through which the probabilistic distribution of the SFT can be obtained.This method was illustrated in detail with an example.Verification studies show that the BML-based method is superior to the traditional inverse velocity method(INVM)and the maximum likelihood method for predicting SFT.The proposed method in this study provides an effective tool for SFT prediction.
文献关键词：
中图分类号：
作者姓名：
Jie Zhang;Zipeng Wang;Jinzheng Hu;Shihao Xiao;Wenyu Shang
作者机构：
Key Laboratory of Geotechnical and Underground Engineering of the Ministry of Education,Tongji University,Shanghai,200092,China;Department of Geotechnical Engineering,Tongji University,Shanghai,200092,China;Natural Science College,Michigan State University,MI,48825,USA
文献出处：
引用格式：
[1]Jie Zhang;Zipeng Wang;Jinzheng Hu;Shihao Xiao;Wenyu Shang-.Bayesian machine learning-based method for prediction of slope failure time)[J].岩石力学与岩土工程学报（英文版）,2022(04):1188-1199
A类：
INVM
B类：
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AB值：
0.494014
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